Locating ambiguities in data

ABSTRACT

A system comprising an associative memory, an input module, a query module, and a display module. The input module is configured to receive a value within a first perspective of the associative memory. The query module is configured to perform an open query of the associative memory using the value, perform the open query within at least one of an insert perspective and a second perspective of the associative memory. The at least one of the insert perspective and the second perspective has as many or more category associations for the value relative to the first perspective. The display module is configured to display a result of the query and to display a list of one or more potential ambiguities that result from the open query.

This application is a continuation of U.S. patent application Ser. No.13/190,212, filed Jul. 25, 2011, status allowed.

BACKGROUND INFORMATION

1. Field

The present disclosure relates generally to associative memorymanagement and in particular to finding the presence of ambiguities indata stored within an associative memory for the purpose of reducinginformation obfuscation which can improve decision making.

2. Background

When analyzing data, ambiguous data can cause confusion, delay, andpossibly errors in an analysis. As used herein, “ambiguous data” may bea set of data that is associated with two or more distinct categories.The “set of data” may be a value, which may be a number, an alphanumericstring of characters, a symbol, or as described elsewhere herein.“Categories” are groupings of data as arranged by a user or a dataprocessing system.

For instance, the number “123-456-7890” could be a phone number, orperhaps could be a part number, or perhaps could be associated with someother category. In this case, the number “123-456-7890” is a “set ofdata” or a value. This set of data is associated with both a phonenumber and a part number. The phone number is a first category and thepart number is a second category. In some cases a user could not know,by viewing the number alone, to which category the number belonged. Or,in a broader sense, the user might not be able to distinguish if thedata might have been ambiguous in the first place, regardless of thepresence of a second category. Thus, a comparison may not be needed inorder to find the presence of ambiguous data.

A multiplicity of ambiguities may arise where the same number sequenceis associated with many different categories, or perhaps specialcharacters such as the two hyphens in 123-456-7890 are ignored by asearch engine, thereby creating even greater numbers of ambiguities.Furthermore, a user may view the set of data as belonging to multiplecategories, thereby increasing the complexity of the data analysis.However, a user may not even be aware of the presence of ambiguous datain a data set, which may be perhaps more problematic.

As indicated above, in some instances, if these ambiguities are notidentified, errors in a data analysis may result. For example, ambiguousdata may provide misleading statistics, or perhaps inaccurate countswhen totaling large amounts of data. In addition, when searching largedata sets, ambiguous data can cloud result sets and cause frustrationwhen a user is trying to obtain useful information. For example, if auser enters the part number into a search engine, the user may see inthe returned results house numbers, phone numbers, and many othercategories which satisfy the number's form, but are of no interest tothe user. Therefore, it may be advantageous for a user to understand ifa possibility of ambiguity exists in one or more data sources in orderto avoid issues with data obfuscation, and thereby improve decisionmaking.

SUMMARY

An embodiment of the present disclosure provides for a system includingan associative memory comprising a plurality of data and a plurality ofassociations among the plurality of data. The plurality of data iscollected into associated groups. The associative memory is configuredto be queried based on at least one relationship, selected from a groupthat includes direct and indirect relationships, among the plurality ofdata in addition to direct correlations among the plurality of data. Thesystem also includes an input module configured to receive a valuewithin a first perspective of the associative memory. The firstperspective comprises a first choice of context for a group of datawithin the plurality of data. The system also includes a query moduleconfigured to perform an open query of the associative memory using thevalue. The query module is further configured to perform the open querywithin at least one of an insert perspective and a second perspective ofthe associative memory. The insert perspective comprises a type ofperspective which is configured to be fed back into the associativememory. The second perspective comprises a second choice of context forthe group of data. The at least one of the insert perspective and thesecond perspective has as many or more category associations for thevalue relative to the first perspective. The system also includes adisplay module configured to display a result of the query. The displaymodule is further configured to display a list of one or more potentialambiguities that result from the open query.

Another advantageous embodiment provides for a computer implementedmethod. The computer implemented method includes receiving a valuewithin a first perspective of an associative memory. The firstperspective comprises a first choice of context for a group of datawithin the plurality of data. The associative memory comprises aplurality of data and a plurality of associations among the plurality ofdata. The plurality of data is collected into associated groups. Theassociative memory is configured to be queried based on indirectrelationships among the plurality of data in addition to directcorrelations among the plurality of data. The computer implementedmethod also includes performing an open query of the associative memoryusing the value, wherein the open query is performed within at least oneof an insert perspective and a second perspective of the associativememory. The insert perspective comprises a type of perspective which isconfigured to be fed back into the associative memory. The secondperspective comprises a second choice of context for the group of data.The at least one of the insert perspective and the second perspectivehas as many or more category associations for the value relative to thefirst perspective. The computer implemented method also includesdisplaying a result of the query, including displaying a list of one ormore potential ambiguities that result from the open query.

Another advantageous embodiment provides for a non-transitory computerreadable storage medium storing computer readable code. The computerreadable code includes computer readable code for receiving a valuewithin a first perspective of an associative memory. The firstperspective comprises a first choice of context for a group of datawithin the plurality of data. The associative memory comprises aplurality of data and a plurality of associations among the plurality ofdata. The plurality of data is collected into associated groups. Theassociative memory is configured to be queried based on indirectrelationships among the plurality of data in addition to directcorrelations among the plurality of data. The computer readable codealso includes computer readable code for performing an open query of theassociative memory using the value. The open query is performed withinat least one of an insert perspective and a second perspective of theassociative memory. The insert perspective comprises a type ofperspective which is configured to be fed back into the associativememory. The second perspective comprises a second choice of context forthe group of data. The at least one of the insert perspective and thesecond perspective has as many or more category associations for thevalue relative to the first perspective. The computer readable code alsoincludes computer readable code for displaying a result of the query,including displaying a list of one or more potential ambiguities thatresult from the open query.

The features, functions, and advantages can be achieved independently invarious embodiments of the present disclosure or may be combined in yetother embodiments in which further details can be seen with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the advantageousembodiments are set forth in the appended claims. The advantageousembodiments, however, as well as a preferred mode of use, furtherobjectives and advantages thereof, will best be understood by referenceto the following detailed description of an advantageous embodiment ofthe present disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a block diagram of a system for finding ambiguities in data inan associative memory, in accordance with an advantageous embodiment;

FIG. 2 is a block diagram showing more details of a system for findingambiguities in data in an associative memory, in accordance with anadvantageous embodiment;

FIG. 3 is a flowchart illustrating a method for finding ambiguities indata in an associative memory, in accordance with an advantageousembodiment;

FIG. 4 is a drawing showing an exemplary software system in use exposingan ambiguity in data in an associative memory, in accordance with anadvantageous embodiment;

FIG. 5 is a flowchart illustrating a method for finding ambiguities indata in an associative memory, in accordance with an advantageousembodiment;

FIG. 6 is a drawing illustrating a relationship between a domain of dataand a perspective view, in accordance with an advantageous embodiment;

FIG. 7 is an exemplary perspective view of a result of a search of anassociative memory, in accordance with an advantageous embodiment;

FIG. 8 is a flowchart illustrating a process of displaying a perspectiveview of an associative memory search result, in accordance with anadvantageous embodiment;

FIG. 9 is a drawing illustrating resource accumulation of results foundas a result of a query on data in an associative memory, in accordancewith an advantageous embodiment;

FIG. 10 is a table illustrating resource accumulation of results foundas a result of a query on data in an associative memory, in accordancewith an advantageous embodiment;

FIG. 11 is a drawing illustrating an insert perspective of associativememories for quick decision making, in accordance with an advantageousembodiment;

FIG. 12 is a drawing illustrating use of an insert perspective of anassociative memory, in accordance with an advantageous embodiment;

FIG. 13 is a table illustrating an error in an associative memory, inaccordance with an advantageous embodiment;

FIG. 14 is a flowchart illustrating a process of dynamically updating anassociative memory, in accordance with an advantageous embodiment.

FIG. 15 is a drawing illustrating a worksheet view for results derivedas a result of querying an associative memory to uncover non-obviousrelationships, in accordance with an advantageous embodiment.

FIG. 16 is an illustration of a data processing system, in accordancewith an advantageous embodiment.

DETAILED DESCRIPTION

The advantageous embodiments recognize and take into account that thepresence of ambiguous data can lead to errors. Thus, the advantageousembodiments provide a mechanism for a user to quickly identify,evaluate, and resolve ambiguous data with respect to one or moredatabases. The advantageous embodiments have many other applications.

For example, the advantageous embodiments may take advantage ofperspectives or views, including insert perspective, to find effectiverelationships, such as shown with respect to FIG. 1 through FIG. 16. Theadvantageous embodiments may be used to uncover ambiguities within anassociative memory, such as shown with respect to FIG. 1 through FIG. 5.The advantageous embodiments may be used to present perspective views ofresults or other data within an associative memory, such as shown withrespect to FIG. 6 through FIG. 8. The advantageous embodiments may beused to find and display resource accumulations, such as shown withrespect to FIG. 9 and FIG. 10. The advantageous embodiments may describeinserting perspectives of associative memories for quick decisionmaking, as shown with respect to FIG. 11 and FIG. 12. The advantageousembodiments may be used for resolving errors in an associative memoryquickly and efficiently, as shown with respect to FIG. 13 and FIG. 14.The advantageous embodiments may be used to display a worksheet view forresults derived as a result of querying an associative memory to uncovernon-obvious relationships, as shown with respect to FIG. 15.

As used herein the term “associative memory” refers to a plurality ofdata and plurality of associations among the plurality of data. The dataand associations may be stored in a non-transitory computer readablestorage medium. The plurality of data may be collected into associatedgroups. The associative memory may be configured to be queried based onindirect relationships among plurality of data in addition to directcorrelations among plurality of data. The associative memory may also beconfigured to be queried based on direct relationships, and combinationsof direct and indirect relationships. Thus, the advantageous embodimentsprovide for an associative memory comprising a plurality of data and aplurality of associations among the plurality of data. The plurality ofdata is collected into associated groups. The associative memory isconfigured to be queried based on at least one relationship, selectedfrom a group that includes direct and indirect relationships, among theplurality of data in addition to direct correlations among the pluralityof data. Associative memory may also take the form of software. Thus,associative memory also may be considered a process by which informationis collected into associated groups in the interest of gaining newinsight based on relationships rather than direct correlation.

As used herein the term “entity” refers to an object that has adistinct, separate existence, though such existence need not be amaterial existence. Thus, abstractions and legal constructs may beregarded as entities. As used herein, there is no presumption that anentity is animate.

As used herein, a “perspective” may be a “point of view.” With respectto an associative memory, a perspective may be a choice of a context fora particular aspect of a user's domain. As used herein, an “insertperspective” is a type of perspective that may be fed back into anassociative memory, and which may be viewable from other perspectives asa possible resource.

As used herein a “domain” may be the subject matter at hand for use ofanalysis.

As used herein the term “ambiguous” may refer to a value or term havingseveral possible meanings or interpretations.

Attention is first turned to finding and/or resolving ambiguous datawithin an associative memory. The advantageous embodiments provide forusing associative memory technology to locate ambiguities by analyzingdata through the remembering of entities associated within a domainspecific, predetermined perspective. The advantageous embodiments maycategorize the results, so the user can quickly determine if theinformation they are seeking contains ambiguity or not.

As described above, an associative memory may base the results of aquery on relationships, associations and frequencies. This property ofassociative memory may be problematic when the underlining data containsambiguous information. This issue may be troublesome within largedomains. In any case, associative memory technologies all have adifficult time distinguishing between ambiguous data.

As indicated above, ambiguous data may increase the difficulty ofaccurately determining if one ambiguous value relates to another. Insuch cases, often the associations created may be erroneous. In turn,erroneous associations may lead to incorrect or confusing results, andmay increase the difficulty of performing a desired analysis with thedesired level of accuracy.

Currently, determining if data within an associative memory is ambiguousrequires a manual investigation initiated by a user's suspicion. Amanual investigation may be extremely time consuming and tedious.Manually uncovering if ambiguity exists within large data sets may bedifficult. For example, uncovering ambiguous data may require anadvanced knowledge of the subject matter to truly determine ifparticular data is ambiguous. In another example, ambiguity might beuncovered only within a context in which the data is situated.Evaluation of context may require excessive time, may require subjectmatter expertise, and may be subject to additional error. Yet further,ambiguous data may be present but a user may be unaware that ambiguousdata may be present, possibly leading to sub-optimal decision making.Additionally, an ambiguity can take different forms in differentcontexts. Still further, errors within data formation can be mistaken asan ambiguity, when in actuality an error is present.

The advantageous embodiments recognize these and other issues. Theadvantageous embodiments may be used to automatically expose thepossibility of ambiguities in data, particularly when using anassociative memory containing the data. The advantageous embodiments maybe used to resolve ambiguities in such data.

The advantageous embodiments also recognize other properties ofassociative memory, and provide for additional improvements with respectto use of associative memory. Thus, the advantageous embodiments alsoprovide for a perspective view of associative memories. The perspectiveview allows for organization of associated entities in a way tofacilitate quick decision making.

The advantageous embodiments also provide for inserting perspectives ofassociative memories for quick decision making. Insert perspectivescreate the ability to insert new unstructured data into an associativememory in order to enable quick decision making. Insert perspectivesalso may allow the user a mechanism for inserting correctinterpretations of data which might otherwise be considered ambiguous.Another advantageous embodiment may provide for resource accumulation ofassociative memories for quick decision making, by allowing users tofocus on the entity with the greatest importance with respect to itsaccumulation.

Another advantageous embodiment provides for using an associative memoryto facilitate a decision based workflow. In this advantageousembodiment, an ability is provided to insert new unstructured data intoknown associative entities in order to improve their associations,allowing other users to leverage the improved associative memory. Thisability also may allow the user a mechanism for inserting correctinterpretations of data which might otherwise be considered ambiguous.

Another advantageous embodiment provides for a worksheet view of anassociative memory. The worksheet view provides an organization ofassociated entities used to uncover non-obvious relationships derivedfrom search result-driven analysis.

Another advantageous embodiment provides for use of associative memorytechnology in intelligence analysis and course of action development.This advantageous embodiment provides for leveraging associative memorytechnology to rapidly evaluate large volumes of free text data, derivesignificant knowledge, and present the knowledge in such a way thatenables an analyst to develop effective operational plans efficiently.

Other advantageous embodiments are also described herein. Thus, theadvantageous embodiments are not limited to the advantageous embodimentsdescribed above.

FIG. 1 is a block diagram of a system for finding ambiguities in data inan associative memory, in accordance with an advantageous embodiment.System 100 shown in FIG. 1 may be implemented using one or more dataprocessing systems, possibly in a distributed or networked environment,and possibly by a group of remotely administered data processing systemsknown as the “cloud.” Each of the one or more data processing systemsthat implement system 100 may be data processing system 1600 describedwith respect to FIG. 16, or variations thereof. System 100 may becharacterized as including one or more modules. Each of these modulesmay be separate or part of a monolithic architecture. System 100 maytake the form of hardware, software, or a combination thereof.

System 100 may include associative memory 102. Associative memory 102may include plurality of data 104 and plurality of associations amongthe plurality of data 106. Plurality of data 104 may be collected intoassociated groups 108. Associative memory 102 may be configured to bequeried based on indirect relationships 110 among plurality of data 104in addition to direct correlations 112 among plurality of data 104.

System 100 may also include input module 114. Input module 114 may beconfigured to receive value 116 within first perspective 118 ofassociative memory 102. First perspective 118 may include first choiceof context for a group of data 120 within plurality of data 104.

System 100 may also include query module 122. Query module 122 may beconfigured to perform an open query of associative memory 102 usingvalue 116. The open query may be performed using open query languagesearch 123. Query module 122 may be further configured to perform theopen query within at least one of insert perspective 124 and secondperspective 126 of associative memory 102. Insert perspective 124 may bea type of perspective 128 which is configured to be fed back intoassociative memory 102. Second perspective 126 may be a second choice ofcontext for the group of data 130. More or fewer perspectives may bepresent. At least one of the insert perspective 124 and the secondperspective 126 may have as many or more category associations 132 forvalue 116 relative to first perspective 118.

System 100 may also include display module 134. Display module 134 maybe configured to display a result of the query 136. Display module 134may be further configured to display list of one or more potentialambiguities 138 that result from the open query by query module 122. Inan advantageous embodiment, one or more of the potential ambiguities inthe list of one or more potential ambiguities 138 may include pluralityof matching attributes 140 for value 116.

The advantageous embodiments shown in FIG. 1 are not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

FIG. 2 is a block diagram showing more details of a system for findingif ambiguities exist within data in an associative memory, in accordancewith an advantageous embodiment. System 200 may show more detail in oneor more components of system 100 of FIG. 1. For example, display module202 of FIG. 2 may be display module 134 in FIG. 1. In another example,result of query 204 in FIG. 2 may be result of query 136 in FIG. 1.Other examples exist with respect to similar terms used in FIG. 1 andFIG. 2. Such similar terms used in FIG. 1 and FIG. 2 may have similarfunctions and similar properties.

System 200 may be implemented using one or more data processing systems,possibly in a distributed or networked environment, and possibly by agroup of remotely administered data processing systems known as the“cloud.” Each of the one or more data processing systems that implementsystem 200 may be data processing system 1600 described with respect toFIG. 16, or variations thereof. System 200 may be characterized asincluding one or more modules. Each of these modules may be separate orpart of a monolithic architecture. System 200 may take the form ofhardware, software, or a combination thereof.

In an advantageous embodiment, value 206 may be first number 208. Listof one or more potential ambiguities 210 may include plurality ofmatching attributes 212. Plurality of matching attributes 212 mayinclude first attribute 214. First attribute 214 may match value 206.Plurality of matching attributes 212 may also include second attribute216. Second attribute 216 may also match value 206. First attribute 214may be a first category 218 associated with first number 208. Secondattribute 216 may be a second category 220 associated with first number208. In an advantageous embodiment, an ambiguity may exist whendetermination 222 cannot be made whether first number 208 should belong,as perceived by a user or a computer program, to first category 218 orto second category 220 by examining only first number 208.

Determination 222 might not be made, in some instances, because initialcategory name 223 does not match or is different from first category218. In this case, an ambiguity in the data may exist or be present. Inother words, if the initial category name entered into a perspective isdifferent from or does not match the category of that perspective, thenambiguous data exists or may be present. For example, if an initialcategory name is “parts”, but the first category is “phone number”, thenambiguous data exists or may be present.

In an advantageous embodiment, value 206 need not be limited to numbers.For example, value 206 may be at least one of set of alphanumericcharacters 224 and set of special characters 226. Special characterswithin set of special characters 226 may include at least one of apunctuation marks, a symbol, a picture, and a character selected from alanguage that uses non-alphabetic characters. Examples of charactersselected from a language that uses non-alphabetic letters may be Chinesecharacters, Japanese Kanji characters, and may further include languagesthat might be characterized as alphabetic in nature but not necessarilyalphanumeric, including Hindi, Sanskrit, Korean, Japanese Kana includingHiragana and Katakana, Russian characters, Arabic characters orcharacters from Arabic-like languages, and any other non-English lettersor non-Arabic numerals used in other written languages.

In an advantageous embodiment, display module 202 may be furtherconfigured to display first category name 228 for first category 218 andsecond category name 230 for second category 220. In anotheradvantageous embodiment, display module 202 may be further configured toprovide first link 232 associated with first category 218 and secondlink 234 associated with second category 220. First link 232 may pointto first information 236 associated with first category 218. Second link234 may point to second information 238 associated with second category220.

The advantageous embodiments shown in FIG. 2 are not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

FIG. 3 is a flowchart illustrating a method for finding ambiguities indata in an associative memory, in accordance with an advantageousembodiment. Process 300 shown in FIG. 3 may be implemented in a module,system, or data processing system, such as system 100 of FIG. 1, system200 of FIG. 2, or data processing system 1600 of FIG. 16. Process 300described with respect to FIG. 3 may be implemented in the form of anon-transitory computer readable storage medium storing computerreadable code which, when implemented by a processor, may execute themethod described with respect to FIG. 3. While the operations of FIG. 3are described as being implemented by a “system,” process 300 is notlimited to being implemented by the systems of FIG. 1 and FIG. 2, butalso may be implemented by one or more real or virtual data processingsystems, possibly in a distributed or networked environment. Process 300may be implemented using hardware, software, or a combination thereof.

In an advantageous embodiment, process 300 begins by the systemreceiving a value within a first perspective of an associative memory(operation 302). In an advantageous embodiment, the first perspectivemay be a first choice of context for a group of data within theplurality of data. The associative memory may be a plurality of data anda plurality of associations among the plurality of data. The pluralityof data may be collected into associated groups. The associative memoryis configured to be queried based on indirect relationships among theplurality of data in addition to direct correlations among the pluralityof data.

Returning to process 300, next the system may perform an open query ofthe associative memory using the value (operation 304). In anadvantageous embodiment, the open query may be performed within at leastone of an insert perspective and a second perspective of the associativememory. The insert perspective may be a type of perspective which isconfigured to be fed back into the associative memory. The secondperspective may be a second choice of context for the group of data. Atleast one of the insert perspective and the second perspective may havemore category associations for the value than the first perspective.

Returning to process 300, the system may display a result of the query,including displaying a list of one or more potential ambiguities thatresult from the open query (operation 306). The one or more potentialambiguities may include a plurality of matching attributes for thevalue.

Process 300 may be varied from the operations described above, andadditional details may be present within any given operation. Forexample, the value may be a first number and the plurality of matchingattributes may be a first attribute that matches the value and a secondattribute that matches the value. The first attribute may be a firstcategory associated with the first number. The second attribute may be asecond category associated with the first number. In an advantageousembodiment, a determination cannot be made by examining only the firstnumber whether the first number should belong, as perceived by a user ora computer program, to the first category or to the second category.This determination might not be made, in some instances, because thevalue or data does not match the initial category type of the initialquery, which might be an entry in first perspective 118 of FIG. 1.

In another advantageous embodiment, the value may include at least oneof a set of alphanumeric characters and a set of special characters. Thespecial characters may include at least one of a punctuation mark, asymbol, a picture, and a character selected from a language that usesnon-alphabetic characters.

In an advantageous embodiment, displaying may further include displayinga first category name for the first category and a second category namefor the second category. In an advantageous embodiment, displaying mayinclude providing a first link associated with the first category and asecond link associated with the second category. The first link maypoint to first information associated with the first category, and thesecond link may point to second information associated with the secondcategory. In another advantageous embodiment, performing the open querymay include performing the open query using an open attribute querylanguage search.

The advantageous embodiments shown in FIG. 3 are not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

FIG. 4 is drawing showing an exemplary software system in use exposingan ambiguity in data in an associative memory, in accordance with anadvantageous embodiment. Drawing 400 is exemplary only, as theadvantageous embodiments may take many different forms and may bedisplayed in many different ways. Thus, drawing 400 is not limiting ofthe claimed inventions. Drawing 400 may be displayed as a result of themethods and techniques described with respect to FIG. 1 through FIG. 3.Drawing 400 may be displayed by using data processing system 1600 or byone or more other data processing systems. As used herein, theadvantageous embodiments describe a “system” as performing one or moreoperations. The “system” may be one or more data processing systems,possibly operating in a distributed or networked environment.

Drawing 400 shows a number of perspectives, including part numberperspective 402, perspective 404, insert perspective 406, andperspective 408. As defined above, a “perspective” may be a “point ofview.” Each associative memory may have one or more perspectives.Drawing 400 may illustrate an associate memory 409 with fourperspectives, perspective 402, perspective 404, insert perspective 406,and perspective 408. Perspective 402 may be a part number perspective.More or fewer perspectives may be present, and different kinds ofperspectives may be present with many different values or arrangementsrelative to those shown in FIG. 4. Each associative memory may have aperspective that enables the insertion of entities. For example, a“problems” perspective or a “complaints” perspective would be a means toinsert problem or complaint data into an associative memory. Thus, withrespect to associative memory 409, a perspective may be a choice of acontext for a particular aspect of a user's domain. As described above,an “insert perspective” is a type of perspective that may be fed backinto associative memory 409.

In a particular advantageous embodiment, not limiting of the claimedinventions, perspective 402 may be a particular part number, such asvalue 410. Although value 410 is shown in FIG. 4, value 410 may take theform of any value and may be a string of alphanumeric characters or anyother symbols, such as those described above with respect to FIG. 1.Furthermore, value 410 might be associated with different categories,other than a part, and may be associated with multiple categories orother corresponding perspectives, such as perspective 404, insertperspective 406, and/or perspective 408.

In an advantageous embodiment, a user or a computer program may entervalue 410 within perspective 402. In this particular advantageousembodiment, which is not limiting on the disclosures or claims, value410 may be a part number. In this advantageous embodiment, a user maypreselect the category associated with the value of value 410 byentering value 410 within perspective 402. The user in this particularadvantageous embodiment may not realize that the data he or she isinvestigating may be ambiguous. The user may wish to gain a betterunderstanding of value 410. In other advantageous embodiments the usermay be able to use this information to resolve ambiguities created bythe possibility that value 410 may be associated with other categorieswithin associative memory 409.

After receiving value 410, the system may use insert perspective 406, asshown at required information 412 of worksheet 414. Another example of aworksheet may be seen with respect to FIG. 15. Then, the system mayperform a query of associative memory 409 within the requiredinformation 412. The query, in one advantageous embodiment, may beperformed using an attribute query language and may be performed usingan open query.

Thus, to identify ambiguous data, the system may perform a lookup on adesired value, such as required information 412, within insertperspective 406. The lookup may work regardless of what the resultcategory is, as long as the entity can be found. Therefore, in aparticular non-limiting advantageous embodiment, the most dependablemethod may be to use insert perspective 406 as the result category,since insert perspective 406 may act as a catalyst to all of the otherperspectives. In this manner, it is possible to identify all ambiguousdata within the domain.

The system then may return all instances of required information 412, asshown at table 416 within worksheet 414. Required information 412 maybe, in an advantageous embodiment, value 410. These instances in table416 may be a result of matching the value of required information 412,which again may be a part number, with other instances of requiredinformation 412 found within associative memory 409. The returned valuesmay be used to identify ambiguity with regard to perspective 402 ofrequired information 412.

Ambiguous data may be displayed in the form of multiple matchingattributes of the result set where the category type of the results donot match a perspective type of the initial query. For example, onecategory type of the results may be “partnumber”, as shown in matchingattributes column 418 of FIG. 4, which matches the category type of theinitial query established by entering value 410 in perspective 402.However, another category type of the results may be “phone”, as shownin matching attributes column 418 of FIG. 4. The category type “phone”does not match the category type “partnumber”; therefore, multiplematching attributes of the result set are present where the categorytype of the results does not match a perspective type of the initialquery. Accordingly, the value “1234567” may represent ambiguous datawithin associative memory 409.

For example, within table 416, matching attributes column 418 may show acategory associated with value 410, along with the value of value 410itself, with the category and value separated by a colon in thisparticular advantageous embodiment. The category value of the matchingattributes of each result may help a user identify ambiguity data. Thus,by looking at a corresponding value, the user can determine if the datais relevant and/or ambiguous. For example, the user may now know thatvalue 410 entered in perspective 402 may represent ambiguous data, inthe sense that the value may be related to two different categories. Inthis example, the value is related to both a part number (the firstcategory) and a phone number (the second category). Thus, the fact thata phone number displays among parts means that value 410 is ambiguous.

In this particular advantageous embodiment, phone number 420 may beconsidered ambiguous data because the value for value 410 also matchesthe value for phone number 420. In other words, the value “1234567”matches two categories simultaneously, both value 410 and phone number420, and thus the value “1234567” may be ambiguous. As a result, haduser not known of this ambiguity, performing some other search oroperation with respect to associative memory 409 using this value mayhave produced an erroneous or undesirable result. Using the advantageousembodiments, the user or a computer program programmed to use theadvantageous embodiments, may now know that the value for value 410 maybe ambiguous with respect to the value for phone number 420. The user orthe computer program may now take appropriate action to either resolvethe ambiguity or take the ambiguous data into consideration whenperforming some other action with respect to associative memory 409.

The system may be provided with additional functionality. For example,the system may be configured to display a source of ambiguous data. Asshown in FIG. 4, the source of any given matching attribute or categoryand corresponding value may be displayed in “{Insert}ID” column 422. Inparticular, for example, source ID 424 may indicate a location or otherlookup value for a source document where the value “1234567” isassociated with the category or attribute “phone.” Likewise, the user ora computer program may use other entries in “{Insert}ID” column 422 tolook up corresponding sources where the corresponding matching attributeand value are referenced.

In an advantageous embodiment, the entries within “{Insert}ID” column422 may be provided with links, hyperlinks, or other pointers to thereferenced source. Thus, in an advantageous embodiment, the user mayselect the source of the ambiguous data in order to see the originalcontext of the ambiguous data. For example, a user or computer programmay use a link, such as by “clicking” on source ID 424, in order todisplay the source material where the value “1234567” is associated witha phone number. Accordingly, the user may determine the ambiguityinvolved and may take other action to resolve or appropriately deal withthe ambiguous data.

Optionally, the system may be configured to display still furtherinformation. For example, the system may display score column 426.Entries within score column 426 may indicate to a user or computerprogram an estimated relevance of the entered value with respect to theperspective in which the value was entered. Thus, for example, arelevance score may be assigned to a particular matching attribute orcategory according to value 410 entered within perspective 402.

The system may be configured to display still further information. Thus,the advantageous embodiments are not limited to the drawings shown inFIG. 4.

Stated differently, the system used with respect to FIG. 4 may performan open attribute query language search on a desired value with thevalue's perspective as the value's result category. Then, the system maydisplay the category value of the matching attributes from its resultset. The system may use the category value to show the ambiguity withregards to the sought value. If the resulting category value of thematching attributes does not match the category of the sought value,then that data is ambiguous. The system also may allow the user to seesome or all of the categories associated with value 410. This abilitycan be helpful in determining why the data is ambiguous and if theambiguous data ought to be corrected or other appropriate action taken.

The advantageous embodiments have several advantages. For example, theadvantageous embodiments may not require an advanced knowledge of thesubject matter to determine if the data is ambiguous. Instead, thesystem may determine this fact and provide or display simple categorizedresults that allow an understanding of how the data might be interpretedas ambiguous.

In another example, the advantageous embodiments may not require theuser to uncover the context behind the data in order to discoverambiguity in the data. Instead, the system may perform this task byusing an associative memory lookup within a predetermined perspective.This lookup may automatically take into account different forms anddifferent contexts, and may provide the user with the correct results.

In another example, the advantageous embodiments may help identify andcorrect errors within the data that might otherwise be mistaken asambiguity. For example, a user may update underlying data identified asbeing ambiguous so that the data is no longer ambiguous.

In another example, the advantageous embodiments may allow users to makequick informed decisions while reducing the worry of introducing dataobfuscation caused by any ambiguity within a given domain. Theadvantageous embodiments may organize the data in a manner that allowsthe user not only to locate possible ambiguities, but to correct oraddress ambiguous data as well. The advantageous embodiments condenseand summarize potentially ambiguous data, so that the user may view onlyone screen rather than multiple pages of data.

The advantageous embodiments may avoid the clutter that typicallyresults when searching for ambiguity. Instead, the advantageousembodiments may provide results focused solely on the problem at hand.The advantageous embodiments may reduce large amounts of data to simplecategories and values. This feature may save a great deal of time when auser has been tasked to explore large data sets.

The advantageous embodiments may allow a user to quickly uncover theambiguous data within their result set. The advantageous embodimentsprovide the search value in the results uncovered.

The advantageous embodiments may also address ambiguous data as apredetermined perspective chosen by the user. The advantageousembodiments may display only the results deemed essential to the user'srequest. The advantageous embodiments may provide a quick mechanism toobtain information used to make quick decisions. The advantageousembodiments need not be domain specific. The advantageous embodimentsmay be platform independent and portable.

The advantageous embodiments may increase reliability through morecomplete data analysis through the discovery and subsequent eliminationof ambiguous data. The advantageous embodiments may increase portabilitythrough flexible deployment of the system. The advantageous embodimentsmay provide increased information accessibility with data organized froma user's perspective. The advantageous embodiments may provide forbetter use of resources, as multiple associative memory tasks may becompleted at one time. The advantageous embodiments may increaseperformance through greater efficacies, such as by showing only datadeemed necessary or only particular types of data, categories of data,or some other organizational principle. The advantageous embodiments mayhave other advantages, as well.

FIG. 5 is a flowchart illustrating a method for finding ambiguities indata in an associative memory, in accordance with an advantageousembodiment. Process 500 may be an alternative to process 300 of FIG. 3.Process 500 shown in FIG. 5 may be implemented in a module, system, ordata processing system, such as system 100 of FIG. 1, system 200 of FIG.2, or data processing system 1600 of FIG. 16. Process 500 described withrespect to FIG. 5 may be implemented in the form of a non-transitorycomputer readable storage medium storing computer readable code which,when implemented by a processor, may execute the method described withrespect to FIG. 5. While the operations of FIG. 5 are described as beingimplemented by a “system,” process 500 is not limited to beingimplemented by the systems of FIG. 1 and FIG. 2, but also may beimplemented by one or more real or virtual data processing systems,possibly in a distributed or networked environment. Process 500 may beimplemented using hardware, software, or a combination thereof.

In an advantageous embodiment, process 500 begins by the systemprompting a user to obtain only relevant information concerning certaindata (operation 502). In order to achieve this goal, the user may desireor need to locate some or all ambiguities within the results of a searchrelated to the data.

Next, the system may receive information regarding the data (operation504). The system may receive this information from the user, or possiblyfrom another computer program. For example, the user may input a valuefor a part number in order to identify ambiguities associated with thatvalue.

Next, the system may use multiple analysis capabilities to produce awide range of entity analytic results relating to the user request(operation 506). The system may then process the input data and maydisplay the list of ambiguous data (operation 508). The system mayclearly identify each entity by showing the category and value of thematching attributes for value 502. The system may consider matchingattribute values that span multiple categories to be ambiguous, as shownin FIG. 4.

Next, the system may prompt a user to decide whether to furtherinvestigate the ambiguous nature of the data (operation 510). If furtherinvestigation is decided, then the user or computer program may examinethe source of ambiguous data to determine how or why the data isambiguous (operation 512). As a result, the user or computer program maydetermine the ambiguity at hand.

Subsequently, or in response to a determination not to furtherinvestigate the ambiguous nature of the data in operation 510, the useror a computer program may make an efficient decision regarding how tohandle the ambiguous data (operation 514). The process may terminatethereafter. The user or computer program can thus focus on relevantknowledge needed to complete a task at hand.

The advantageous embodiments shown in FIG. 5 are not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

FIG. 6 is drawing illustrating a relationship between a domain of dataand a perspective view, in accordance with an advantageous embodiment.The drawing of FIG. 6 may be implemented using associative memory 600,which may be associative memory 102 of FIG. 1. A perspective is asdescribed with respect to FIG. 1 and FIG. 4.

Domain 602 may contain various disparate data 604 within associativememory 600. FIG. 6 illustrates how disparate data 604 may be organizedinto perspective view 606. For example, among all of disparate data 604,certain data elements may be organized into perspective view 606.Perspective view 606 may be perspective view 700 of FIG. 7. For example,data elements 608, 610, 612, 614, and 616 may be organized intoperspective view 606, as shown by lead lines 620.

The advantageous embodiments may use associative memory technologycoupled with a custom user interface to enable quick decision makingfrom result-driven analysis through the remembering of entitiesassociated with a domain specific, predetermined perspective. Theadvantageous embodiments may organize the results in an efficient mannerthat helps users discover relationships and associations within thatperspective in order to make quick decisions.

Attention is now turned to issues that make desirable an improvedpresentation of an associative memory query result. When faced withmaking quick decisions, analysts may desire to quickly uncover specificinformation among large data sets. This specific information preferablymay be presented in a way that eliminates time used unnecessarily ondetails considered superfluous, while allowing the analyst to focus ononly the data elements deemed needed or desired for accuratedecision-making.

Focusing an analysis using associative memory technology may bedifficult due to a possibly overwhelming number of results that may beuncovered using an associative memory. Associative memory technology maywork with large amounts of data and thereby create problems for userswhen attempting to navigate a result set of a given domain, especiallywhen forced to make quick decisions. Associative memory technology maybase its results on relationships, associations and frequencies, whichmay be confusing when attempting to find quick answers to particularproblems within a large domain.

Thus, the results given from using an associative memory technology cancloud a user's investigation with data the user may considersuperfluous. For example, when searching for a particular part, a usermay be overwhelmed with data from operators, models, service requests,maintenance data, and customer messages. These distractions can causethe user to lose sight of the original task or problem to be solved. Theability to keep centered on the problem at hand may be very difficult,especially when the associative memory technology provides so much datawith countless navigation possibilities. These aspects of associativememories may deter quick decision making.

Nevertheless, associative memory technology may encapsulate multipleanalysis capabilities to aid users. Multiple analysis capabilities mayinclude collecting attributes, associations, and similarities amongdata. Each capability may take form as separate result sets, which maymake it difficult for the user to compare values among the result sets.Some results may span different categories and may lead the user awayfrom their initial problem set. In addition, many of these results maytake a significant amount of time and effort to uncover, because theresults may include a combination of tasks. As a result, associativememory technology may be considered a poor tool with respect to quickdecision making, especially when dealing with large data sets where auser can get lost and bewildered by results generated by the associativememory.

Two possible approaches to these issues may include use of a Web searchtool and use of a complex intelligence tool. With respect to a Websearch tool to search requested information, this type of search mayreturn references to documents containing information on the requestedinformation. However, many false positives and false negatives mayresult, and information may not be displayed in a useful fashion. Inturn, complex intelligence tools may require an expert to analyze theresults in which the time line can vary.

In either case, solving these types of problems previously involved alengthy, manual process. The user may have to find a way through thedata without losing sight of the initial request. In some cases, thisprocess might require the use of additional software. For example, usersmight employ an external tool or application and copy the results intoit. In any case, neither the use of a Web search tool or a complexintelligence tool would be useful.

Another way of navigating results is to simplify the results.Simplification could include narrowing the user's domain until thedomain is essentially the same as the user selected perspective.Subsequently, a user could also widen their perspective until theperspective equals the domain. Either one of these procedures may causethe user to modify their search, which most users may prefer not to do.

FIG. 7 is an exemplary perspective view of a result of a search of anassociative memory, in accordance with an advantageous embodiment. Theadvantageous embodiment of FIG. 7 may represent a screenshot displayedas a result of such a search. Perspective view 700 may be a result of asearch or query conducted according to the techniques described withrespect to FIG. 1 through FIG. 6. Perspective view 700 may beassociative memory 600 of FIG. 6. Perspective view 700 need not belimited to displaying results of queries or searches performed using anassociative memory, but may also be used to display the results ofqueries or searches performed using Web search engines and/or complexintelligence tools. The advantageous embodiments may allow users tofocus on key information in order to make informed decisions quickly.Perspective 701 may be a perspective, such as perspective 402 of FIG. 4.Perspective 701 may be a parts perspective, as shown, but may be anyperspective and is not limited to the advantageous embodiments shown inFIG. 7.

Perspective view 700 may be logically separated into different sections,separated by lines or any other convenient section designation system.Each section displays certain information. The sections may be arrangedto aid a user to more quickly find relevant information. The sectionsshown in FIG. 7 are exemplary only, and may be varied. For example, thesections shown in FIG. 7 need not be one section stacked on another, asshown, but could be arranged in different patterns and may be bounded bydifferent shapes, colors, or other distinguishing characteristics.

In the advantageous embodiments shown in FIG. 7, seven sections areshown, stacked one on top of the other. However, as described above,this arrangement may be varied.

In an advantageous embodiment, section 702 may display an initial entitycategory and the value that was searched. In a non-limiting example,section 702 could display the entity category along with its value. Inthis advantageous embodiment the entity category may be “part number”and the value may be “12345678”. A user may provide this value in orderto gain a better understanding of the subject matter at hand. The usermay preselect the entity category, or perspective, associated with thisvalue. As long as the user stays within this perspective, all theresults will take that category's form.

In an advantageous embodiment, section 704 may display attribute cloudvalues of the value sought in section 702. Attribute cloud values may bebroad categories or values associated with the sought value. Attributecloud values may provide a quick summary by supplying a list ofimportant words accumulated during the search relating to the valuesought in section 702.

In an advantageous embodiment, section 706 may display associated entityvalues of the sought value in section 702. These entity values may beadditional entities associated with the sought value in section 702.These entity values may be similar to the attributes above, except theymay carry more weight as entities, as their category type may be definedby the user. In addition, these entity types could also be defined asperspectives, if the user deemed them as being perspectives. The usermay search some or all of these values as well.

In an advantageous embodiment, section 708 may display keyword valuesassociated with the sought value in section 702. The values may besimilar to the attributes above in section 704, except these values maycarry more weight as keywords, but less weight than entities. Thesevalues in section 704 may have been identified by the users as relevantbut not as perspectives. In some advantageous embodiments, the usercannot search these values.

In an advantageous embodiment, section 710 may display one or morevalues of one or more entity categories associated with the sought valuein section 702. These values in section 710 may be entities associatedwith the sought value, and may share the same category value. The valuesin section 710 may be similar to the attributes above in section 704,except the values in section 710 may carry more weight as entitiesand/or a perspective. In an advantageous embodiment, the user may searchthese values as well.

In an advantageous embodiment, section 712 may display snippets.Snippets may be portions of text or graphics of content related to thesought value in section 702. Thus, section 712 may provide the user witha snapshot of the resources pertaining to the sought value. Preferably,section 712 may show a brief fragment of the underlying source data,solely to show the user where the data initially came from. However,section 712 may show additional underlying data, even completeunderlying data. Section 712 might in some instances be expandable orcontractible to show more or less underlying data.

In an advantageous embodiment, section 714 may display other entityvalues that may be like the sought value shown in section 702. Thus, thevalues displayed in section 714 may share the same attributes as thesought value. This feature gives the user the ability to quickly locatevalues just like the one sought. The system may rank and order thesevalues in section 714 by similarity, or by some other organizationscheme, possibly placing the item most similar to the sought value insection 702 at the top of section 714.

Section 714 may display the matching attributes which the commonentities share. However, section 714 may show additional data to helpguide the user to make better informed decisions. For example, in anadvantageous embodiment, the system may display the price of a part, sothat a user could select the least expensive alternative among similarentities.

In summary, in some cases it may be important to categorize results whensearching vast amounts of data, especially when using associative memorylearning agent technology. The results gathered from this technology canbe very difficult to manage and navigate. The advantageous embodimentsmay focus on the user perspective to help a user or a querying computerprogram to obtain the most out of associative memory technology. Theadvantageous embodiments may organize the data into a well thought-outinterface that may provide the most useful data that the user may desireto review. The advantageous embodiments may show more or fewer data.

The advantageous embodiments also may capture entity analytics from theperspective that is most valuable to the end user. The advantageousembodiments may organize the data so a user can explore the result setwithout deviating from an initial query. The advantageous embodimentsmay perform multiple entity analytic tasks and displays all of theresults all at once within a perspective that may be most valuable tothe end user. The advantageous embodiments may keep the results centeredon the initial category of the sought value. This feature may allow theuser to rapidly navigate among the returned information.

Thus, the advantageous embodiments provide users with a quick mechanismto obtain a summary of information all at once. The advantageousembodiments may display only key and important words in an intuitivemanner. The advantageous embodiments may place the most useful datafirst, allowing users to gain knowledge before reading through anymaterial. The advantageous embodiments may provide a great advantagewhen faced with large amounts of data.

Because the advantageous embodiments may display syntax that is commonlyused, a user can peruse through data without having to fully understandthat data. The advantageous embodiments may display nouns, verbs,adjectives, and adverbs, or possibly pictures, symbols, video, or audio.In this manner, the advantageous embodiments may allow a user to quicklygrasp the general concept behind the information being sought.

Web search engines do not possess these capabilities. Results returnedby Web search engines usually include documents, or large portions oftext that have to be analyzed. To obtain the type of results needed tomake accurate decisions, an analyst would have to spend a great deal oftime reading and understanding the material. This process would not bevery effective for quick decision-making. Additionally, Web searchengines have limited searching capabilities. Web search engines mayperform static-like searches and are not capable of producing resultslike the ones found from entity rich associations found in associativememory technologies.

Likewise, complex intelligence tools do not possess the capabilities ofthe advantageous embodiments. The results derived from these types oftools may require a great deal of analysis and time as too much data isreturned. Complex intelligence tools also typically require a subjectmatter expert to be able to use the tools.

Thus, both Web-based searches and complex intelligence tools may involvea number of manual steps to reproduce the results gathered using theadvantageous embodiments. Manual steps or analysis is usuallycumbersome, distracting, and time consuming, as well as error prone.Manual analysis may be fraught with the possibility of navigatingfurther away from the user's initial request, and going in a directionthat is astray from original user intent.

Additionally, the advantageous embodiments may avoid the clutter thatmay result from typical entity analytic queries. Thus, the advantageousembodiments may provide results within a single perspective pre-selectedby the user. This feature may cause the results to focus solely on theproblem at hand. As a result, the advantageous embodiments may reducelarge amounts of data to simple words and search terms. This feature maysave a great deal of time when exploring large data sets.

The advantageous embodiments may provide a quick mechanism to obtain keyand important information used to make quick decisions. The advantageousembodiments may perform multiple entity analytic tasks and display allof the results in one location on the screen. The advantageousembodiments may provide a well thought out interface that is easy tonavigate.

The advantageous embodiments need not be domain specific. Theadvantageous embodiments may be platform independent and portable. Theadvantageous embodiments may be flexible in that the advantageousembodiments may allow users to add and remove perspectives, as well aslogical units.

Thus, the advantageous embodiments may allow users to make quick,informed decisions without having to analyze an undesirable amount ofdata. The advantageous embodiments may organize the data to provide themost important information first. This information may be condensed andsummarized so that the information only requires the user to read a fewwords rather than multiple pages.

The advantageous embodiments may also be a tool for trainingindividuals. The advantageous embodiments may allow trainees to focusonly on the key information needed to perform their individual tasks.The advantageous embodiments may generalize an undesirable amount ofdata into comprehensible words, thereby shortening the time spent neededfor training.

Unlike other entity-based search tools, the advantageous embodimentsprovide a high degree of flexibility. The advantageous embodiments maybe suited to any domain that utilizes entity analytics. Once a domain ischosen, a user may select any perspective to explore within that domain.Users may add and remove perspectives as well as logical units. Thisfeature also allows the advantageous embodiments to be rapidlydeployable.

FIG. 8 is a flowchart illustrating a process of displaying a perspectiveview of an associative memory search result, in accordance with anadvantageous embodiment. Process 800 shown in FIG. 8 may be implementedin a module, system, or data processing system, such as system 100 ofFIG. 1, system 200 of FIG. 2, or data processing system 1600 of FIG. 16.Process 800 described with respect to FIG. 8 may be implemented in theform of a non-transitory computer readable storage medium storingcomputer readable code which, when implemented by a processor, mayexecute the method described with respect to FIG. 8. While theoperations of FIG. 8 are described as being implemented by a “system,”process 800 is not limited to being implemented by the systems of FIG. 1and FIG. 2, but also may be implemented by one or more real or virtualdata processing systems, possibly in a distributed or networkedenvironment. Process 800 may be implemented using hardware, software, ora combination thereof.

Process 800 may begin by receiving user input (operation 802). The inputreceived from the user may be within a preselected perspective. In anadvantageous embodiment, the user may have been tasked to obtainspecific knowledge within a large domain of information within apreselected perspective. The user may input a specific informationrequest in order to gain a better understanding of the subject matter athand. For example, the user may have been tasked to obtain knowledgeabout a part number, where “partnumber” is the preselected perspective.Thus, as part of receiving user input the user may input the partnumber.

Next, the associative memory may perform multiple analyses upon the userinput to produce a wide range of entity analytic results (operation804). The system may then receive the entity analytic results andformulate them for display (operation 806). As part of formulating theentity analytic results, the system may organize the entity analyticresults as described above to aid the user to make decisions quickly andaccurately.

Next, the system may return organized data for display to a user(operation 808). In an advantageous embodiment, the user or anothercomputer program may use the results to make an accurate and rapiddecision by only reviewing the data displayed (operation 810).

The advantageous embodiments shown in FIG. 8 are not meant to implyphysical or architectural limitations to the manner in which differentadvantageous embodiments may be implemented. Other components inaddition and/or in place of the ones illustrated may be used. Somecomponents may be unnecessary in some advantageous embodiments. Also,the blocks are presented to illustrate some functional components. Oneor more of these blocks may be combined and/or divided into differentblocks when implemented in different advantageous embodiments.

FIG. 9 is a drawing illustrating resource accumulation of results foundas a result of a query on data in an associative memory, in accordancewith an advantageous embodiment. The resource accumulation shown indrawing 900 may be implemented using an associative memory and themethods and devices described with respect to FIG. 1 through FIG. 3, aswell as data processing system 1600 of FIG. 16. Drawing 900 illustratesa mechanism for accumulating resource information and data in such amanner to allow users to make quick decisions regarding which data ismost relevant to the user's task, and possibly not ambiguous.

As with FIG. 6, the resource accumulation shown in drawing 900 of FIG. 9may be implemented using associative memory 902, which may beassociative memory 102 of FIG. 1. Domain 904 may contain variousdisparate data 906 within associative memory 902. FIG. 9 illustrates howdisparate data 604 of FIG. 6 may be organized by resource accumulation,as shown by table 908. Table 908 may be an example of table 1000 in FIG.10. For example, among all of disparate data 906, certain data elementsmay be organized into table 908. For example, data elements 910, 912,914, 916, and 918 may be organized into table 908, as shown by leadlines 920.

Table 908 shows data organized by a total number of instances found andsources of where the data may be found. The more instances found, themore likely the data may be relevant to the user. Thus, the user's timemay be more efficiently used searching what is probably the mostrelevant information. Likewise, the user may concentrate on searching ina source or sources having the most number of hits with respect to thesought-after data. On the other hand, users would not need to spend anundesirable amount of time by searching information with low counts or alow number of returns. Naturally, the user may use the information intable 908 in other ways.

Thus, the advantageous embodiments may use associative memory technologycoupled with a custom user interface to enable quick decision-making byanalyzing resource accumulations through the remembering of entitiesassociated within a domain specific, predetermined perspective. Theadvantageous embodiments may organize the results so the user can focuson the entity with the greatest accumulation of information in order tomake a quick decision concerning that perspective.

For example, suppose a supplier provides an analyst with a list of partsneeded for a front landing gear. The analyst may rely on his or herexperience or domain knowledge to decide on which part to locate first.If the analyst were unfamiliar with a given part, it is possible theanalyst would postpone processing of that part regardless of itsimportance. The analyst may also process this list sequentially orrandomly.

The advantageous embodiments may avoid the clutter that may result fromtypical entity analytic queries. Clutter, caused from an informationoverload, can overwhelm the user and make it difficult to evaluate theinformation at hand. The advantageous embodiments may circumvent thisoccurrence by providing results within a single perspective preselectedby the user. Thus, the advantageous embodiments may cause the results tofocus solely on the problem at hand. The advantageous embodiments mayreduce large amounts of data to simple accumulations and counts. Thisresult may save a great deal of time when tasked to explore large datasets.

Thus, the advantageous embodiments may allow a user to quickly uncoverthe most valuable data within their result set. The advantageousembodiments may determine a value through accumulations or counts ofassociations, as shown in FIG. 9.

The advantageous embodiments have several advantages. For example, theadvantageous embodiments may address problems in a predeterminedperspective chosen by the user. The advantageous embodiments may displayonly the resource accumulation essential to the user's request. Theadvantageous embodiments may provide a quick mechanism to obtain key andimportant information used to make quick decisions. The advantageousembodiments may not be domain specific. The advantageous embodiments maybe platform independent and portable. The advantageous embodiments maybe flexible in that the advantageous embodiments may allow users toselect single or multiple search terms, as well as add and remove datasources. The advantageous embodiments may increase the speed of dataanalysis, thereby saving time and money.

As an example, suppose the bottom of a wing of an airplane is to beserviced. An analyst could collect a list of parts within the wing. Theanalyst could then use the advantageous embodiments to determine themost important parts, based on their associations, and concentrate onthose parts first as those parts have been predetermined to be the mostimportant. This procedure could save the analyst time because many ofthese parts could be difficult to locate. By pre-ordering the parts, thetime used to service the aircraft wing could be reduced.

FIG. 10 is a table illustrating resource accumulation of results foundas a result of a query on data in an associative memory, in accordancewith an advantageous embodiment. Table 1000 may be an example of table908 in FIG. 9. Table 1000 may be table 908 of FIG. 9, for example.

Column 1002 may present a desired value or a list of desired values. Forexample, column 1002 could represent a part or a list of parts ofinterest to the user. Column 1002 may be an initial entity categorywithin preselected perspective 1001. The values in column 1002 may beprovided by a computer program, or may be provided by a user. The valuesin column 1002 may be provided in order to gain a better understandingof the subject matter at hand. The values in column 1002 may be a singleentity or a list of entities. The user may preselect the categoryassociated with these values. As long as the user stays within theassigned perspective, all the accumulated results will take thatcategory's form.

Columns 1004, 1006, 1008, and 1010 represent resource values of the datawithin the domain. Thus, the values in columns 1004, 1006, 1008, and1010 may identify all resources or data sources within the domain. Eachvalue may represent an individual source that contains the count of datain column 1002 specified within the domain. Typically, these sources maybe divided into logical units, defined by the user. Essentially, thesesources may contain the underlining data that created the associativememories.

In turn, the cells within columns 1004, 1006, 1008, and 1010 mayindicate total number of instances a given value in column 1002 occurswithin the corresponding data source. Thus, for example, the second cellin column 1004 has a value of “11.” This value means that 11 instancesof part “XYZ” shown in the first cell of column 1002 occur within“source 1” shown in the first cell of column 1004.

Thus, the values shown in the cells of columns 1004, 1006, 1008, and1010 may identify accumulated resources or counts of associations withineach data source. The values indicate “interest” by pointing out theirusage through accumulated counts, as shown in column 1012. Theadvantageous embodiments may sort these results to bring the greatestvalue to the top, as shown in cell 1014 of column 1004.

In other words, the value in cell 1014 may identify the total resourceaccumulation among all of the data sources within an associative memory.The value in cell 1014 may represent the “total interest” in the desiredvalue. Values with large numbers of total resource counts in cell 1014may indicate there are numerous associations to this value in column1002 and therefore one might conclude the value is important. Likewise,values with small numbers of total resource counts in cell 1014 mayindicate there are fewer associations to this value in column 1002 andtherefore one might conclude the value is less or not important.

The various values shown in table 1000 are exemplary only. The values,numbers, categories, and other aspects of table 1000 may be varied asdesired.

The advantageous embodiments may work with single entities as well aslists of entities. In either case, the analyst can quickly determine theinterest or importance of returned information. The analyst may alsodetermine what additional information to pursue.

In addition, the advantageous embodiments may display the count of eachresource where few or no counts occur within a corresponding source.This feature may, in some cases, tend to indicate less value orimportance.

Thus, the advantageous embodiments may employ a prioritizing techniquethat one can apply to any data set without having to deeply analyze thedata first. The advantageous embodiments do not require a user to relyon previous experiences or past knowledge in order to prioritize theinformation within their data set. The advantageous embodiments mayperform this task.

The advantageous embodiments may also provide a user with a sorted listof priorities based on perceived importance. The sorted list ofpriorities may be gathered from associations within the data. Byproviding accumulations or counts, the advantageous embodiments mayavoid difficulty in determining the importance of items when presentedwith limited or excessive amounts of data.

Attention is now turned to an example of the advantageous embodiments inuse. First, the user may be tasked to obtain knowledge within a largedomain. Next, the user may input a specific information request in orderto gain a better understanding of the subject matter at hand. Forexample, the user may be tasked to obtain knowledge about a list ofdamaged part numbers.

Next, the user may use associative memory technology to incorporatemultiple analysis capabilities to produce a wide range of entityanalytic results relating to the user request. The advantageousembodiments may then be used to process the input data and generatecounts of accumulated resources for the user. The advantageousembodiments may organize or sort these accumulations by value or“interest”, as shown in FIG. 10. Finally, the advantageous embodimentsmay return the sorted accumulated data to the user, so the user mayfocus their interest on the information that has the greatest value.Alternatively, if no value contains high counts, the user can quicklysee that the data may be of limited value. Finally, a quick decision maybe made by doing a detailed review of the information that is likely tohave the most valuable information.

FIG. 11 is a drawing illustrating an insert perspective of associativememories for quick decision making, in accordance with an advantageousembodiment. The perspective insertion shown in drawing 1100 may beimplemented using an associative memory and the methods and devicesdescribed with respect to FIG. 1 through FIG. 3, as well as dataprocessing system 1600 of FIG. 16. Drawing 1100 illustrates a mechanismfor incorporating an insert perspective into an associative memory insuch a manner to allow users to make quick decisions.

The advantageous embodiments may employ a cohesive approach to insertingunstructured information into an associative memory technology on anad-hoc basis. This feature enables quick decision-making based on resultdriven analysis through the remembering of entities associated within adomain specific, predetermined insert perspective. The advantageousembodiments may organize the results in an efficient manner that helpsusers discover relationships and associations in order to make timelydecisions concerning newly acquired information. Thus, the advantageousembodiments may provide a dynamic way of understanding newly introduceddata from an analytical point of view. The advantageous embodiments mayemploy a user interface application which may allow a user to interactmore quickly and efficiently with an associative memory technology.

In an advantageous embodiment, the user may insert new information intothe system, via insert perspective 1104. Each associative memory has aninsert perspective type that acts as a catalyst and provides a feedbackmechanism for all of the other perspectives. Insert perspective 1104 maytake advantage of this fact to enable insertions into an associativememory of domain 1106. The data may then be manipulated by associativememory 1102 using insert perspective 1104, with the result organizedfrom the point of view of user's perspective 1108.

FIG. 12 is a drawing illustrating use of an insert perspective of anassociative memory, in accordance with an advantageous embodiment. Setof drawings 1200 may be implemented using a system, such as those shownin FIG. 1 through FIG. 3, as well as data processing system 1600 of FIG.16. Set of drawings 1200 may be used to demonstrate use of insertperspective 1104 of FIG. 11. Associative memory 1202 may be anassociative memory as described elsewhere herein, including but notlimited to associative memory 102 of FIG. 1.

In an advantageous embodiment, drawing 1204 may be data to be insertedinto associative memory 1202, such as an incident report, an email, anews article, or any material relating to the domain at hand. Drawing1204 may display or represent new knowledge or information, previouslynot in the system. For example, the information could be a recentarticle, email, or some type of incident report. In any case, theinformation displayed in drawing 1204 is new and should be inserted intoassociative memory 1202 in order to obtain a better understanding of thesubject matter.

Drawing 1206 shows an exemplary procedure for inserting the informationfrom drawing 1204 into associative memory 1202. In an advantageousembodiment, the user may insert the new information into the system viaa graphical user interface. The user may copy or otherwise input thetextual information from drawing 1204 into the space provided. Theadvantageous embodiments may use a single insert perspective type to adddata, such as insert perspective 1208. Each associative memory 1202 mayhave an insert perspective type 1208 that may act as a catalyst andprovide a feedback mechanism for all of the other perspectives.

Next, the information in insert perspective 1208 is processed. The newlyinserted information may be processed and synchronized with datacurrently in the system. The newly inserted information becomes part ofthe associative memory.

At this point, the information may be organized or reorganized from auser's perspective, as shown in drawing 1210. Thus, the resulting data,possibly organized in a perspective view, may help the user understandthe information that was initially presented. The results may be brokeninto easily understandable words and terms that may be quicklyinterpreted. The advantageous embodiments may divide the results in anumber of different ways.

In an advantageous embodiment, the results may be divided with an insertentity type with an insert identification. This inserted information maybe automatically assigned a value in order to identify the information.This value may be consistent with the other inserted entity types withinthe system.

The results may also be divided using attribute cloud values of theinserted information. These values, possibly in the perspective view,may be attributes associated with the inserted information. These valuesmay provide a quick summary by supplying a list of important wordsaccumulated during the insertion.

The results may also be divided using the associated entity values,possibly in the perspective view, of the inserted information. Thesevalues may be entities associated with the inserted information. Thesevalues may be similar to the attributes above, except these values maycarry more weight as entities. The user may search all or some of thesevalues, if needed or desired.

The results may also be divided using associated keyword values,possibly in the perspective view, of the inserted information. Thesevalues may be keywords associated with the inserted information. Thesevalues may be similar to the attributes above, except they may carrymore weight as keywords, but less than entities.

The results may also be divided by using associated values, possibly inthe perspective view, of the entity category of the insertedinformation. These values may be entities associated with the insertedinformation and may share the same category value. These values may besimilar to the attributes above, except they may carry more weight asentities. The user may search these values as well.

The results may also be divided by showing snippets, possibly in theperspective view, of underlying source data. This feature may providethe user with a snapshot of the resources pertaining to the insertedinformation. In this case, what is displayed is the location of thesource of the attributes, entities, and keywords. In an advantageousembodiment, only a brief fragment of the data may be shown.

The results may also be divided by other entity values, possibly in theperspective view, like the inserted information. These values may sharethe same attributes as the inserted information. This feature may givethe user the ability to quickly locate values just like the oneinserted. The advantageous embodiments may rank and order these valuesby similarity, placing the most similar at the top.

Attention is now turned to another advantageous embodiment in use.First, new unstructured information is presented to the user. The newunstructured information may take many forms. For instance, the newunstructured information may be an incident report.

Next, the user may be tasked to investigate new information recentlydiscovered. For example, a user may be tasked to examine a recentlyreleased incident report concerning an in-flight phenomenon.

The user then inserts information into the system. For example, the usermay input the incident report into an associate memory in order to gaina better understanding of the incident. To accomplish this task, theuser may copy and paste the incident report into the system using agraphical user interface, such as that shown in drawing 1206.

Instead, the user may cause associative memory 1202 to use multipleanalysis capabilities to produce a wide range of entity analytic resultsrelating to the user request. Normally, associative memory 1202 mayprocess incoming data through a predefined channel. In an advantageousembodiment, the user may bypass the normal data flow process because ofthe information's dynamic nature.

Next, the advantageous embodiments may process the input datadynamically and formulate its results for the user. The advantageousembodiments may organize the results to enable quick decision-making, asshown in drawing 1210.

The advantageous embodiments may then return the associated informationorganized from a user's perspective. A quick, efficient, and correctdecision may then be made by focusing on the necessary or most desirableknowledge useful for completing the task at hand.

The advantageous embodiments may resolve issues regarding analyzingnewly acquired data by means of an associative memory technology. Inparticular, the advantageous embodiments provide a user with the abilityto investigate new unstructured data that covers both breadth and depth,in order to make quick and accurate decisions.

For example, a flight engineer may obtain a report describing anin-flight phenomenon incident that occurred the night before on anairplane the flight engineer serviced. The flight engineer may want toexamine the report to try to understand what happened and how to respondappropriately to the phenomenon. The flight engineer may want to comparethe incident to any previous and related phenomenon to determine if acommon solution exists for the current phenomenon. However, to exploitthis information accurately in a quick manner using an associativememory is very difficult.

Following the example used above, the flight engineer may take thefollowing actions. First, the flight engineer may read the report. Theflight engineer would then locate keywords within the report to bestquery a database or associative memory to find historical data. Theflight engineer may then compare the relevancy of historical data toform a decision.

However, there are numerous drawbacks to these steps. First, the entireprocess is likely to be time consuming and is not geared towards quickdecision-making. Second, the process may be error prone and somewhatdependent on the knowledge of the reader. Third, any mistakes or wrongcomparisons could lead the user to make an incorrect or incompletedecision.

In addition, when working with associative memory technology, theestablished protocol may be to gather data first and then analyze thedata. In cases where new data is introduced, the new data first must beloaded into the system and then processed in order to be used. This factmakes it difficult for an analyst to obtain immediate results from areport that does not follow the normal automated data flow.

The advantageous embodiments recognize these and other issues andsatisfactorily addresses them. The advantageous embodiments describedwith respect to FIG. 12 may allow users to make quick, informeddecisions without having to pour over massive amounts of newlyintroduced data. By using an insert perspective to enter data, and thenorganize the data according to the user's perspective, these goals maybe accomplished.

More particularly, the advantageous embodiments may organize the newdata in a way that provides the most important information first. Thisinformation may be condensed and summarized so that the user may read afew words rather than multiple pages. The advantageous embodiments mayalso provide transparency typically not seen with most associativememory insertion techniques. Instead, the advantageous embodiments mayclearly identify what is being inserted, the status of that insertion,and the results gathered from its analysis.

Another advantage of the advantageous embodiments is the use of a singleperspective to insert data. The advantageous embodiments may insert alldata, regardless of its nature, in the same way. This feature may act asa single entry point into the associative memory, thus reducingerroneous or incomplete insertions into multiple locations.

FIG. 13 is a table illustrating an example of an error in an associativememory, in accordance with an advantageous embodiment. The advantageousembodiments described with respect to FIG. 13 may demonstrate how it maynot be possible to change data once it is introduced to an associativememory.

For example, table 1300 may represent data and relationships stored inassociative memory 1302. However, associative memory 1302 need not storethe data shown in table 1300 in the manner shown in FIG. 13. Table 1300or associative memory 1302 may take the form of a non-transitorycomputer readable storage medium, such as persistent storage 1608 orcomputer readable storage media 1624 of FIG. 16. The data in table 1300or associative memory 1302 may be manipulated by a processor, such asprocessor unit 1604 of FIG. 16. FIG. 13 demonstrates an error in table1300 that may be addressed using procedures similar to those describedwith respect to FIG. 12. One distinction, however, may be that the newinformation may be automatically associated with the original entity towhich it was added.

Table 1300 is organized according to columns 1304 and rows 1306. Columns1304 represent different entities, represented for example only byliving organisms. Thus, for example, column 1308 contains entriesrelating to whales.

In turn, rows 1306 represent different entity relationships with respectto an entity represented by columns 1304. Thus, for example, row 1310may relate to a classification of a living entity represented in columns1304.

Attention is now turned to the error in table 1300. Cell 1312corresponds to the intersection of column 1308 and row 1310, meaningthat cell 1312 should contain the classification type (row 1310) of theliving organism “whale” (column 1308). The data in cell 1312 indicatesthat a “whale” is a “fish.” This entry is incorrect, because a whaleshould have been properly classified as a mammal. Upon discovering thiserror, a user may wish to update table 1300 and/or associative memory1302 so that the correct classification of a “whale” is properlyrepresented.

This type of error may be corrected advantageously using proceduressimilar to those described with respect to FIG. 12, with one distinctionpossibly being that the new inserted information may be automaticallyassociated with the original entity to which it was added. However,attention is first turned to why this type of error correction may benon-trivial with respect to associative memory 1302.

The advantageous embodiments may solve an associative memory workflowissue. Once information, such as the incorrect data in cell 1312, is inassociative memory 1302, there is no way to leverage outside analysis ofthat information within the system. As a result, the error may be verydifficult to correct.

The advantageous embodiments provide for a feedback mechanism thatallows users to insert additional information concerning the erroneousdata. Once introduced, the new information may alter the data'sassociations immediately, and thereby provide results that are morealigned with what the user wants to see.

For example, consider the error shown in cell 1312 of FIG. 13. Users maydiscover this error every time this sequence is reexamined. This errorwould continue to occur until a user, typically an administrator, fixedthe entirety of associative memory 1302. However, fixing the entirety ofassociative memory 1302 may be a non-trivial, time-consuming process.

Aside from fixing the entirety of associative memory 1302, users havefew options. A user might leave the information unchanged, but then eachother user may rediscover the problem. This technique may be aninefficient use of resources and may be very time consuming.

A user could communicate to others, through a different means such asverbal or written, the information they intended to impose onassociative memory 1302. However, this technique divides theinformation, keeping part of the information in the memory and part ofthe information somewhere else. This technique could be confusing whenretrieving the information.

A user could rely on public knowledge, industry standards, or commonsense, for example, so that other users would recognize the true natureof the error. For example, the fact that a whale is not a fish, butrather is a mammal, is public knowledge. However, this technique mayassume everyone shares the same public knowledge, industry standards,and common sense. Unfortunately, this assumption may not always bevalid.

All of these techniques fail to address the workflow problem accurately.However, the advantageous embodiments may incorporate the user's inputimmediately into the system, so that everyone can leverage his or herknowledge immediately. The user input may take the form of corrections,additional details, explanations, relative work experience, symbols,pictures, audio files, or any other data.

The new information may also be referred to as added information. Theadded information may be dynamically correlated to the original entityand results formulated for the user. These procedures may be performedusing the techniques described above with respect to FIG. 12.

In an advantageous embodiment, the system may provide an analysis of theadded information first, allowing the user to compare the addedinformation with other entities similar to the added information. Thenthe user can navigate back to the original entity and view the updatedassociations created from the additional information. Subsequently, theuser or a computer program may make a quick decision by focusing on theknowledge needed or desired to complete the task at hand. Furthermore,other users may leverage the added information because the addedinformation is now part of the system. The added information may besorted so the added information appears first, thereby permitting othersto reap the added information's benefits quickly.

Attention is now returned to FIG. 12, though the drawings are now usedto demonstrate correction of errors or modification of information asdescribed with respect to FIG. 13. In an advantageous embodiment, a usermay add new information, such as for example to correct the error incell 1312 of FIG. 13. The user may add the new information via a graphicuser interface, such as drawing 1206 of FIG. 12. In particular, the usermay add the new information via insert perspective 1208 of FIG. 12.

This system may then use a single insert perspective type to add thedata. As described above, each associative memory may have an insertperspective type that may act as a catalyst and provide a feedbackmechanism for all of the other perspectives. This insert perspective mayenable insertions.

In an advantageous embodiment, the newly added information is processedby the system. In particular, the system may synchronize the newly addeddata with data currently in the system. Thus, the newly added data maybecome part of the associative memory.

The system then provides a link back to the original entity. In thismanner, the user may navigate back and view the updated associationscreated from the additional information. This information may be viewedin drawing 1210 of FIG. 12.

Drawing 1210 may also include a snapshot of the inserted information. Inanother advantageous embodiment, the most recently added data may bedisplayed first within drawing 1210.

In an advantageous embodiment, the new information may be reorganizedfrom a user perspective. The resulting data, organized in a familiarmanner, may help the user understand the newly added information. Theresults may be subdivided or sorted into easily understandable words andterms that a user can quickly interpret.

Thus, the advantageous embodiments may enable users to add newinformation to existing entities. Once added, the new information maybecome an entity itself, allowing a user to analyze the new informationin the same manner as other information.

One distinction, however, may be that the new information may beautomatically associated with the original entity to which the newinformation was added. This feature may permit the user to navigate backto the new information in order to see what effect the added informationmay have had on the original entity. A user may assign additionalpredefined associations by updating the memory's data schema andprogramming interface.

Thus, the advantageous embodiments may provide a complete workflow thataccommodates dynamic decisions. The advantageous embodiments may allowusers to insert new information pertaining to an entity without havingto upset an associative memory or the flow of the decision-makingprocess. Furthermore, the advantageous embodiments may incorporate theuser's input immediately into the system. Once introduced, the newinformation may propagate through the entire system and may beimmediately available for everyone to use.

FIG. 14 is a flowchart illustrating a process of dynamically updating anassociative memory, in accordance with an advantageous embodiment.Process 1400 shown in FIG. 14 may be implemented in a module, system, ordata processing system, such as system 100 of FIG. 1, system 200 of FIG.2, or data processing system 1600 of FIG. 16. Process 1400 describedwith respect to FIG. 14 may be implemented in the form of anon-transitory computer readable storage medium storing computerreadable code which, when implemented by a processor, may execute themethod described with respect to FIG. 14. While the operations of FIG.14 are described as being implemented by a “system,” process 1400 is notlimited to being implemented by the systems of FIG. 1 and FIG. 2, butalso may be implemented by one or more real or virtual data processingsystems, possibly in a distributed or networked environment. Process1400 may be implemented using hardware, software, or a combinationthereof.

Process 1400 may begin with the system prompting a user to obtainknowledge within a large domain (operation 1402). This operation may beoptional. For example, a user may decide to perform this task upon theuser's own initiative, or may be given the task by some other person.The task may take any form. However, in a non-limiting example, the taskmight take the form of obtaining information concerning a reportregarding a type of automobile.

Next, the system may receive a user request for data (operation 1404).In an advantageous embodiment, the user may request specific informationin order to gain a better understanding of the subject matter at hand.For example, the user may be tasked to obtain knowledge about anautomobile report. Thus, the system may receive user input of anidentification number for the automobile report.

Next, the system may return information relating to the user request(operation 1406). The returned information may be based on a query tothe associative memory that is based on the user input. Upon inspection,the user may decide the information returned is not sufficient for theuser's purposes. As a result, the system receives additional user inputin the form of additional information in order to obtain better results,from the perspective of the user.

The user may then determine that new or updated information should beadded to the returned data. As a result, the system may receive new orupdated information regarding the returned data (operation 1408). Thenew or updated information could be a correction, further analysis, oradditional details. For example, a user may desire to add an automotiverecall analysis to the results obtained in order to influence theassociations. The recall analysis may cite direct informationcorrelating with the automobile report.

Optionally, the system may then bypass normal data flow (operation1410). The system may bypass the normal data flow process because of theinformation's dynamic nature and the information's direct correlation tothe entity at hand.

The system may then produce entity analytic results relating to the userrequest (operation 1412). In an advantageous embodiment, the system mayspecifically use the multiple analysis capabilities of an associativememory to produce a wide range of results. Had the normal data flow beenused at operation 1410, then the system may have processed the userrequest through a predefined channel, which may have limited the resultsobtained.

Next the system may process the new or updated information dynamically(operation 1414). The term “process the new or updated informationdynamically” may mean in some cases that the associative memorycorrelates the new or updated information to the original entity, andthen the system may formulate the results for the user. The formulatedresults may be displayed according to the advantageous embodimentsdescribed elsewhere herein, such as but not limited to FIG. 4, FIG. 7,FIG. 12, FIG. 13, and FIG. 15.

Next, the system may return an analysis of the added information(operation 1416). The user may then compare the new or updatedinformation with other similar entities. The user may then navigate backto the original entity and view the updated associations created fromthe new or updated information.

Optionally, the system may then prompt the user to enter any decisionsmade (operation 1418). This operation may be optional in the sense thatthe user may take some action outside the system. For example, the usermay make a quick decision relating to the task by focusing only on themost relevant knowledge to complete the task at hand.

Also optionally, other users may leverage the new or updated information(operation 1420). Other users can leverage the new or updatedinformation because it is now part of the associative memory. The new orupdated information may be sorted so that new or updated informationappears first. In this manner, other users may quickly take advantage ofthe benefits offered by the new or updated information.

For example, the system may receive a new request for data (operation1422). The system may then process the user new request for data usingthe associative memory (operation 1424). The system may then return thenew associated information (operation 1426). Optionally, the system mayprompt the user to input a decision (operation 1428). However, thisdecision may be taken outside of the system. The process may terminatethereafter.

FIG. 15 is a drawing illustrating a worksheet view for results derivedas a result of querying an associative memory, in accordance with anadvantageous embodiment. Worksheet 1500 may be, in an advantageousembodiment, a worksheet showing results displayed to a user. The resultsmay be of a query performed on an associative memory. Worksheet 1500 maybe implemented using an associative memory, such as associative memory102 of FIG. 1 or any of the other associative memories described herein.System 100 of FIG. 1 may also be used to implement worksheet 1500. Dataprocessing system 1600 of FIG. 16 may be used to generate, display,print, and otherwise manage worksheet 1500. Worksheet 1500 may be analternative view of data in an associative memory. Worksheet 1500 mayrepresent, in some advantageous embodiments, a graphical user interfacepresented to a user.

Applicants first address why worksheet 1500 may represent an advantageover current techniques for displaying results of searches when queryingan associative memory. When searching a problem domain for solutions,users tend to narrow their focus on the perspective at hand. As aresult, a user may fail to notice that a broader problem and solutionmay exist.

For instance, an engineer searching for a solution to a specific wiringimprovement on a vehicle might fail to notice a larger improvement maybe performed with respect to that entire year's production of vehicles,or perhaps that a larger improvement may need to be performed at aparticular vehicle factory location. Instead, by focusing only on aspecific instance of the wiring improvement, the engineer may return tothe issue multiple times before noticing a pattern that was not obviousupon initial investigation. After each return, the engineer may spendunnecessary time attempting to perform the same procedure on a differentvehicle, only to realize the issue was much larger than originallyperceived.

This happenstance may occur even with widely known problems, because ofthe tendency of some people to focus so closely on the problem at hand.Many times, users may overlook what, at a later date, might be obviousin hindsight. For example, repeated wiring improvements at a particularfacility may have been performed faster than at other facilities. Aftera long investigation, the improvement was tracked down to a singleelectrician whose wiring procedures were unexpected. Thus, a way toimprove the speed of wiring improvements at all facilities, inhindsight, would be to learn from the single electrician's unexpectedwiring procedures. However, the change in procedures would not have beenobvious otherwise, and some other cause may have been attributed to thefacility's success.

The advantageous embodiments take these considerations into account andprovide a systematic way of presenting information in order to find andhighlight underlying trends that may not be obvious at first, but may beobvious later in hindsight. An exemplary procedure might be as follows.

First, a user may be tasked to obtain knowledge concerning an issuewithin a large domain. The user may then describe the problem in orderto gain a better understanding of the subject matter at hand. Forexample, a mechanic may be tasked to obtain knowledge concerningimprovements to an automobile's front coil spring. The mechanic may usea graphical user interface to input information describing the issue.

The user may use an associative memory technology to incorporatemultiple analysis capabilities to produce a wide range of entityanalytic results relating to the coil spring issue. The advantageousembodiments may then display the results and formulate them for theuser. The advantageous embodiments may organize the results into twocategories, the entity type associations and all the associations. Theseassociations may provide the user with valuable information that aidstheir decision.

The advantageous embodiments may return a list of entity type results,whose source contains attributes that match with the terms in theproblem description. For example, if a user wanted to locate parts for a“front assembly”, the returned list would contain those parts withmatching terms “front” or “assembly” or both.

The advantageous embodiments may also return a list of all entity typeresults as well. These results may provide the user with valuableinformation which may be non-obvious. The user may be provided withvaluable information for which the user did not know to ask. Forexample, if a user wanted to locate parts for a front assembly, thereturned list may contain not only parts, but also other entities suchas models, operators, and others. This feature is valuable because thisfeature forces the user to look outside the user's perspective to see ifthere is a broader issue and/or a greater solution or approach. As aresult, the user may make a quick decision by obtaining only thenecessary data needed to complete the task at hand.

In an advantageous embodiment, the user may start with a problemdescription entered in perspective 1501. Part or all of this descriptionmay appear in a title for worksheet 1500, such as title 1502.Alternatively, title 1502 may refer to any information, not necessarilythe problem description. One or more input sections may be present. Forexample, in the advantageous embodiment shown in FIG. 15, the inputsections may be required input section 1504, optional input section1506, and excluded input section 1508. For this example, the user mayselect “coil” as a required term and “spring,” “front,” and “tire” asoptional. The user may experiment with these sections to see whichcombination of search terms may yield the best results as perceived bythe user. As indicated above, more or fewer input sections may bepresent, and some, none, or all of the information entered into theinput sections may appear within title 1502.

Required input section 1504 also may be used to enter requiredinformation with respect to performing a query using an open attributequery language. “Required information” is determined with respect to auser's perception of what is “required” for a given search, and is notlimiting of the advantageous embodiments. These values may be requiredterms in an issue description. Information entered in required inputsection 1504 may match exactly in order to be counted towards theassociated results.

Optional input section 1506 may be used to input optional information.The values entered in optional input section 1506 may be optional termsin the issue description. The term “optional” is determined with respectto a user's perception of what is “optional” for a given search, and isnot limiting of the advantageous embodiments. Information entered inoptional input section 1506 may not be required to match exactly inorder to be counted towards the association results. Instead, if theuser enters multiple terms, each term is evaluated independently.

Excluded input section 1508 may be used to input information to beexcluded from a query. Thus, the values entered in excluded inputsection 1508 may represent excluded terms in the issue description. Theterm “excluded” is determined with respect to a user's perception ofwhat is “excluded” for a given search, and is not limiting of theadvantageous embodiments. Information entered in excluded input section1508 may exclude answers from the result list where the result'sattribute list contains one or more of these terms.

More or fewer input sections may be present. For example, an additionalinput section may be provided in order to receive an instruction toperform a query of an associative memory. Thus, for example, selectingor otherwise actuating the additional input section may be used toinitiate an association search.

In the example illustrated in FIG. 15, the results returned by theinvention may be divided into two categories. The first category may beassociated report 1510, which corresponds to the preselectedperspective. Associated report 1510 may show a list of reports where theattributes within each report matches with the original issuedescription. At this point, the user may investigate each report to seeif the user might gain some insight into the task at hand.

The second category may be associated information 1512. Associatedinformation 1512 may provide additional information for which the usermight not have thought to search. Further investigation showed that themanufacturer improved 9-3 model 1514 of the product due to its improvedcoil springs. This fact would have probably become obvious to the userafter investigating each report. Nevertheless, the visibility providedin the second category shows the issue description has a highcorrelation to the attributes of 9-3 model 1514.

This conclusion implies that the real issue regarding the task at handmay be in the entire series of 9-3 model 1514, not just in a fewreports. This knowledge may save the user a lot of time when determininghow to improve the model at hand, by providing the user with betterinformation when making quick and informed decisions.

In addition, associated information 1512 may show non-obviousrelationships that exist between the desired entities and all the otherentities within the associative memory. Many of these relationships mayyield positive correlations that may surprise most users by callingattention to potentially larger problems.

Thus, the advantageous embodiments may provide a systematic and uniformapproach to identifying solutions, even those that exist outside of auser's immediate perspective. The advantageous embodiments also mayprovide users with information they “didn't ask for”, allowing the usersto draw previously unconsidered conclusions. The advantageousembodiments may also present the user with non-obvious relationships,which in turn helps the user to better understand the entire issuedomain.

Worksheet 1500 may have other advantages. For example, the advantageousembodiments may address problems in a predetermined perspective chosenby the user, but show results in all the perspectives. The advantageousembodiments may display only the key aspects essential to the user'srequest, at the option of the user and as “key” and “essential” aredetermined by the user. The advantageous embodiments may provide a quickway to obtain information used to make quick decisions. The advantageousembodiments may perform multiple entity analytic tasks and display allof the results in one location on a display. The advantageousembodiments need not be domain specific. The advantageous embodimentsmay be platform independent and portable. The advantageous embodimentsmay be flexible in that the advantageous embodiments may allow users tosave and remove worksheets. The advantageous embodiments are not limitedto the above.

Turning now to FIG. 16, an illustration of a data processing system isdepicted in accordance with an advantageous embodiment. Data processingsystem 1600 in FIG. 16 is an example of a data processing system thatmay be used to implement the advantageous embodiments, such as system100 of FIG. 1, or any other module or system or process disclosedherein. In this illustrative example, data processing system 1600includes communications fabric 1602, which provides communicationsbetween processor unit 1604, memory 1606, persistent storage 1608,communications unit 1610, input/output (I/O) unit 1612, and display1614.

Processor unit 1604 serves to execute instructions for software that maybe loaded into memory 1606. Processor unit 1604 may be a number ofprocessors, a multi-processor core, or some other type of processor,depending on the particular implementation. A number, as used hereinwith reference to an item, means one or more items. Further, processorunit 1604 may be implemented using a number of heterogeneous processorsystems in which a main processor is present with secondary processorson a single chip. As another illustrative example, processor unit 1604may be a symmetric multi-processor system containing multiple processorsof the same type.

Memory 1606 and persistent storage 1608 are examples of storage devices1616. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, data,program code in functional form, and/or other suitable informationeither on a temporary basis and/or a permanent basis. Storage devices1616 may also be referred to as computer readable storage devices inthese examples. Memory 1606, in these examples, may be, for example, arandom access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 1608 may take various forms,depending on the particular implementation.

For example, persistent storage 1608 may contain one or more componentsor devices. For example, persistent storage 1608 may be a hard drive, aflash memory, a rewritable optical disk, a rewritable magnetic tape, orsome combination of the above. The media used by persistent storage 1608also may be removable. For example, a removable hard drive may be usedfor persistent storage 1608.

Communications unit 1610, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 1610 is a network interface card. Communicationsunit 1610 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 1612 allows for input and output of data with otherdevices that may be connected to data processing system 1600. Forexample, input/output unit 1612 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 1612 may send output to a printer. Display1614 provides a mechanism to display information to a user.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 1616, which are in communication withprocessor unit 1604 through communications fabric 1602. In theseillustrative examples, the instructions are in a functional form onpersistent storage 1608. These instructions may be loaded into memory1606 for execution by processor unit 1604. The processes of thedifferent embodiments may be performed by processor unit 1604 usingcomputer implemented instructions, which may be located in a memory,such as memory 1606.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in processor unit 1604. The program code in thedifferent embodiments may be embodied on different physical or computerreadable storage media, such as memory 1606 or persistent storage 1608.

Program code 1618 is located in a functional form on computer readablemedia 1620 that is selectively removable and may be loaded onto ortransferred to data processing system 1600 for execution by processorunit 1604. Program code 1618 and computer readable media 1620 formcomputer program product 1622 in these examples. In one example,computer readable media 1620 may be computer readable storage media 1624or computer readable signal media 1626. Computer readable storage media1624 may include, for example, an optical or magnetic disk that isinserted or placed into a drive or other device that is part ofpersistent storage 1608 for transfer onto a storage device, such as ahard drive, that is part of persistent storage 1608. Computer readablestorage media 1624 also may take the form of a persistent storage, suchas a hard drive, a thumb drive, or a flash memory, that is connected todata processing system 1600. In some instances, computer readablestorage media 1624 may not be removable from data processing system1600.

Alternatively, program code 1618 may be transferred to data processingsystem 1600 using computer readable signal media 1626. Computer readablesignal media 1626 may be, for example, a propagated data signalcontaining program code 1618. For example, computer readable signalmedia 1626 may be an electromagnetic signal, an optical signal, and/orany other suitable type of signal. These signals may be transmitted overcommunications links, such as wireless communications links, opticalfiber cable, coaxial cable, a wire, and/or any other suitable type ofcommunications link. In other words, the communications link and/or theconnection may be physical or wireless in the illustrative examples.

In some advantageous embodiments, program code 1618 may be downloadedover a network to persistent storage 1608 from another device or dataprocessing system through computer readable signal media 1626 for usewithin data processing system 1600. For instance, program code stored ina computer readable storage medium in a server data processing systemmay be downloaded over a network from the server to data processingsystem 1600. The data processing system providing program code 1618 maybe a server computer, a client computer, or some other device capable ofstoring and transmitting program code 1618.

The different components illustrated for data processing system 1600 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different advantageousembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 1600. Other components shown in FIG. 16 can be variedfrom the illustrative examples shown. The different embodiments may beimplemented using any hardware device or system capable of runningprogram code. As one example, the data processing system may includeorganic components integrated with inorganic components and/or may becomprised entirely of organic components excluding a human being. Forexample, a storage device may be comprised of an organic semiconductor.

In another illustrative example, processor unit 1604 may take the formof a hardware unit that has circuits that are manufactured or configuredfor a particular use. This type of hardware may perform operationswithout needing program code to be loaded into a memory from a storagedevice to be configured to perform the operations.

For example, when processor unit 1604 takes the form of a hardware unit,processor unit 1604 may be a circuit system, an application specificintegrated circuit (ASIC), a programmable logic device, or some othersuitable type of hardware configured to perform a number of operations.With a programmable logic device, the device is configured to performthe number of operations. The device may be reconfigured at a later timeor may be permanently configured to perform the number of operations.Examples of programmable logic devices include, for example, aprogrammable logic array, programmable array logic, a field programmablelogic array, a field programmable gate array, and other suitablehardware devices. With this type of implementation, program code 1618may be omitted because the processes for the different embodiments areimplemented in a hardware unit.

In still another illustrative example, processor unit 1604 may beimplemented using a combination of processors found in computers andhardware units. Processor unit 1604 may have a number of hardware unitsand a number of processors that are configured to run program code 1618.With this depicted example, some of the processes may be implemented inthe number of hardware units, while other processes may be implementedin the number of processors.

As another example, a storage device in data processing system 1600 isany hardware apparatus that may store data. Memory 1606, persistentstorage 1608, and computer readable media 1620 are examples of storagedevices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 1602 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 1606, or a cache, such asfound in an interface and memory controller hub that may be present incommunications fabric 1602.

The different advantageous embodiments can take the form of an entirelyhardware embodiment, an entirely software embodiment, or an embodimentcontaining both hardware and software elements. Some embodiments areimplemented in software, which includes but is not limited to forms,such as, for example, firmware, resident software, and microcode.

Furthermore, the different embodiments can take the form of a computerprogram product accessible from a computer usable or computer readablemedium providing program code for use by or in connection with acomputer or any device or system that executes instructions. For thepurposes of this disclosure, a computer-usable or computer readablemedium can generally be any tangible apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer usable or computer readable medium can be, for example,without limitation an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, or a propagation medium. Non limitingexamples of a computer readable medium include a semiconductor or solidstate memory, magnetic tape, a removable computer diskette, a randomaccess memory (RAM), a read-only memory (ROM), a rigid magnetic disk,and an optical disk. Optical disks may include compact disk—read onlymemory (CD-ROM), compact disk—read/write (CD-R/W) and DVD.

Further, a computer usable or computer readable medium may contain orstore a computer readable or usable program code such that when thecomputer readable or usable program code is executed on a computer, theexecution of this computer readable or usable program code causes thecomputer to transmit another computer readable or usable program codeover a communications link. This communications link may use a mediumthat is, for example without limitation, physical or wireless.

A data processing system suitable for storing and/or executing computerreadable or computer usable program code will include one or moreprocessors coupled directly or indirectly to memory elements through acommunications fabric, such as a system bus. The memory elements mayinclude local memory employed during actual execution of the programcode, bulk storage, and cache memories which provide temporary storageof at least some computer readable or computer usable program code toreduce the number of times code may be retrieved from bulk storageduring execution of the code.

Input/output or I/O devices can be coupled to the system either directlyor through intervening I/O controllers. These devices may include, forexample, without limitation to keyboards, touch screen displays, andpointing devices. Different communications adapters may also be coupledto the system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Non-limiting examplesare modems and network adapters are just a few of the currentlyavailable types of communications adapters.

Thus, the advantageous embodiments address the issue of findingrelationships in a vast plurality of data in order to make specificdecisions regarding particular situations. The advantageous embodimentsutilize associative memory technology to perform such tasks.

The advantageous embodiments may take advantage of perspectives or view,including insert perspective, to find effective relationships, such asshown with respect to FIG. 1 through FIG. 16. The advantageousembodiments may be used to find and/or resolve ambiguous data within anassociative memory, such as shown with respect to FIG. 1 through FIG. 5.The advantageous embodiments may be used to present perspective views ofresults or other data within an associative memory, such as shown withrespect to FIG. 6 through FIG. 8. The advantageous embodiments may beused to find and display resource accumulations, such as shown withrespect to FIG. 9 and FIG. 10. The advantageous embodiments may describeinsert perspective of associative memories for quick decision making, asshown with respect to FIG. 11 and FIG. 12. The advantageous embodimentsmay be used for finding and resolving errors in an associative memoryquickly and efficiently, as shown with respect to FIG. 13 and FIG. 14.The advantageous embodiments may be used to display a worksheet view forresults derived as a result of querying an associative memory, as shownwith respect to FIG. 15. The advantageous embodiments may have manyother uses and applications.

The description of the different advantageous embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different advantageousembodiments may provide different advantages as compared to otheradvantageous embodiments. The embodiment or embodiments selected arechosen and described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. A system comprising: a processor; an associativememory comprising a plurality of data and a plurality of associationsamong the plurality of data, wherein the plurality of data is collectedinto associated groups, and wherein the associative memory is configuredto be queried based on at least one relationship, selected from a groupthat includes direct and indirect relationships, among the plurality ofdata in addition to direct correlations among the plurality of data; aninput module configured to receive a value within a first perspective ofthe associative memory, wherein the first perspective comprises a firstchoice of context for a group of data within the plurality of data; aquery module configured to perform an open query of the associativememory using the value, wherein the query module is further configuredto perform the open query within at least one of an insert perspectiveand a second perspective of the associative memory, wherein the insertperspective comprises a type of perspective which is configured to befed back into the associative memory, wherein the second perspectivecomprises a second choice of context for the group of data, and whereinthe at least one of the insert perspective and the second perspectivehas as many or more category associations for the value relative to thefirst perspective; and a display module configured to display a resultof the open query, wherein the display module is further configured todisplay a list of one or more potential ambiguities that result from theopen query.
 2. The system of claim 1, wherein the one or more potentialambiguities comprises a plurality of matching attributes for the value.3. The system of claim 2, wherein the value comprises a first number andwherein the plurality of matching attributes comprises a first attributethat matches the value and a second attribute that matches the value,wherein the first attribute comprises a first category associated withthe first number, wherein the second attribute comprises a secondcategory associated with the first number, and wherein a determinationcannot be made by examining only the first number whether the firstnumber should belong, as perceived by a user or a computer program, tothe first category or to the second category.
 4. The system of claim 2,wherein the value comprises at least one of a set of alphanumericcharacters and a set of special characters, wherein the specialcharacters comprise at least one of a punctuation marks, a symbol, apicture, and a character selected from a language that usesnon-alphabetic characters.
 5. The system of claim 3, wherein the displaymodule is further configured to display a first category name for thefirst category and a second category name for the second category. 6.The system of claim 3, wherein the display module is further configuredto provide a first link associated with the first category and a secondlink associated with the second category, wherein the first link pointsto first information associated with the first category, and wherein thesecond link points to second information associated with the secondcategory.
 7. The system of claim 1, wherein the open query is performedusing an open attribute query language search.
 8. The system of claim 3,wherein the one or more potential ambiguities arise because differencesexist between the first category and an initial category name whenperforming the determination.
 9. The system of claim 1, wherein, withrespect to the associative memory, the first perspective is a choice ofa context for a particular aspect of a domain.
 10. The system of claim1, wherein, a first category of an entity and the first perspective arepreselected and are associated with the value.
 11. A computerimplemented method comprising: receiving a value within a firstperspective of an associative memory, wherein the first perspectivecomprises a first choice of context for a group of data within aplurality of data, wherein the associative memory comprises a pluralityof data and a plurality of associations among the plurality of data,wherein the plurality of data is collected into associated groups, andwherein the associative memory is configured to be queried based on atleast one relationship, selected from a group that includes direct andindirect relationships, among the plurality of data in addition todirect correlations among the plurality of data; performing an openquery of the associative memory using the value, wherein the open queryis performed within at least one of an insert perspective and a secondperspective of the associative memory, wherein the insert perspectivecomprises a type of perspective which is configured to be fed back intothe associative memory, wherein the second perspective comprises asecond choice of context for the group of data, and wherein the at leastone of the insert perspective and the second perspective has as many ormore category associations for the value relative to the firstperspective; and displaying a result of the open query, includingdisplaying a list of one or more potential ambiguities that result fromthe open query.
 12. The computer implemented method of claim 11, whereinthe one or more potential ambiguities comprises a plurality of matchingattributes for the value.
 13. The computer implemented method of claim12, wherein the value comprises a first number and wherein the pluralityof matching attributes comprises a first attribute that matches thevalue and a second attribute that matches the value, wherein the firstattribute comprises a first category associated with the first number,wherein the second attribute comprises a second category associated withthe first number, and wherein a determination cannot be made byexamining only the first number whether the first number should belong,as perceived by a user or a computer program, to the first category orto the second category.
 14. The computer implemented method of claim 12,wherein the value comprises at least one of a set of alphanumericcharacters and a set of special characters, wherein the specialcharacters comprise at least one of a punctuation marks, a symbol, apicture, and a character selected from a language that usesnon-alphabetic characters.
 15. The computer implemented method of claim13, wherein displaying further comprises displaying a first categoryname for the first category and a second category name for the secondcategory.
 16. The computer implemented method of claim 13, whereindisplaying further comprises providing a first link associated with thefirst category and a second link associated with the second category,wherein the first link points to first information associated with thefirst category, and wherein the second link points to second informationassociated with the second category.
 17. The computer implemented methodof claim 11, wherein performing the open query comprises performing theopen query using an open attribute query language search.
 18. Thecomputer implemented method of claim 13, wherein the one or morepotential ambiguities arise because differences exist between the firstcategory and an initial category name when performing the determination.19. A non-transitory computer readable storage medium storing computerreadable code comprising: computer readable code for receiving a valuewithin a first perspective of an associative memory, wherein the firstperspective comprises a first choice of context for a group of datawithin a plurality of data, wherein the associative memory comprises aplurality of data and a plurality of associations among the plurality ofdata, wherein the plurality of data is collected into associated groups,and wherein the associative memory is configured to be queried based onat least one relationship, selected from a group that includes directand indirect relationships, among the plurality of data in addition todirect correlations among the plurality of data; computer readable codefor performing an open query of the associative memory using the value,wherein the open query is performed within at least one of an insertperspective and a second perspective of the associative memory, whereinthe insert perspective comprises a type of perspective which isconfigured to be fed back into the associative memory, wherein thesecond perspective comprises a second choice of context for the group ofdata, and wherein the at least one of the insert perspective and thesecond perspective has as many or more category associations for thevalue relative to the first perspective; and computer readable code fordisplaying a result of the open query, including displaying a list ofone or more potential ambiguities that result from the open query. 20.The non-transitory computer readable storage medium of claim 19, whereinthe one or more potential ambiguities comprises a plurality of matchingattributes for the value.